Benchmarking reports

from sklearn_benchmarks.report import Reporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

reporting = Reporting(config_file_path="config.yml")
reporting.run()

Time report

	hour	min	sec
algo
KNeighborsClassifier	0.0	35.0	29.427319
daal4py_KNeighborsClassifier	0.0	2.0	24.818884
KNeighborsClassifier_kd_tree	0.0	2.0	33.711494
daal4py_KNeighborsClassifier_kd_tree	0.0	0.0	27.477394
KMeans_tall	0.0	0.0	22.950139
daal4py_KMeans_tall	0.0	0.0	8.519828
KMeans_short	0.0	0.0	2.721255
daal4py_KMeans_short	0.0	0.0	1.365602
LogisticRegression	0.0	0.0	19.975836
daal4py_LogisticRegression	0.0	0.0	4.250008
Ridge	0.0	0.0	10.925706
daal4py_Ridge	0.0	0.0	2.045437
HistGradientBoostingClassifier	0.0	5.0	4.819674
lightgbm	0.0	5.0	12.822656
xgboost	0.0	5.0	16.501516
catboost	0.0	6.0	0.082929
total	1.0	3.0	42.503401

KNeighborsClassifier vs daal4py

All estimators share the following hyperparameters:

	value
algorithm	brute

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.294	0.000	2.724	0.000	1	100	NaN	NaN	0.496	0.000	0.592	0.000	See	See
1	KNeighborsClassifier	predict	1000000	1000	100	NaN	24.804	0.465	0.000	0.025	1	100	0.950	0.736	1.700	0.012	14.594	0.292	See	See
2	KNeighborsClassifier	predict	1000000	1	100	NaN	0.220	0.003	0.000	0.220	1	100	1.000	1.000	0.091	0.000	2.419	0.035	See	See
3	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.146	0.000	5.482	0.000	-1	5	NaN	NaN	0.489	0.000	0.299	0.000	See	See
4	KNeighborsClassifier	predict	1000000	1000	100	NaN	35.668	0.000	0.000	0.036	-1	5	0.813	0.820	1.703	0.012	20.948	0.145	See	See
5	KNeighborsClassifier	predict	1000000	1	100	NaN	0.185	0.014	0.000	0.185	-1	5	1.000	1.000	0.091	0.001	2.046	0.158	See	See
6	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.141	0.000	5.685	0.000	1	1	NaN	NaN	0.488	0.000	0.288	0.000	See	See
7	KNeighborsClassifier	predict	1000000	1000	100	NaN	13.630	0.039	0.000	0.014	1	1	0.736	0.820	1.704	0.022	7.997	0.105	See	See
8	KNeighborsClassifier	predict	1000000	1	100	NaN	0.205	0.001	0.000	0.205	1	1	1.000	1.000	0.092	0.001	2.239	0.019	See	See
9	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.142	0.000	5.620	0.000	-1	100	NaN	NaN	0.490	0.000	0.290	0.000	See	See
10	KNeighborsClassifier	predict	1000000	1000	100	NaN	35.352	0.000	0.000	0.035	-1	100	0.950	0.936	1.758	0.008	20.104	0.087	See	See
11	KNeighborsClassifier	predict	1000000	1	100	NaN	0.185	0.013	0.000	0.185	-1	100	1.000	1.000	0.092	0.000	2.023	0.141	See	See
12	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.140	0.000	5.722	0.000	1	5	NaN	NaN	0.490	0.000	0.286	0.000	See	See
13	KNeighborsClassifier	predict	1000000	1000	100	NaN	24.074	0.082	0.000	0.024	1	5	0.813	0.936	1.754	0.003	13.729	0.051	See	See
14	KNeighborsClassifier	predict	1000000	1	100	NaN	0.212	0.001	0.000	0.212	1	5	1.000	1.000	0.091	0.000	2.316	0.017	See	See
15	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.137	0.000	5.856	0.000	-1	1	NaN	NaN	0.489	0.000	0.279	0.000	See	See
16	KNeighborsClassifier	predict	1000000	1000	100	NaN	25.759	0.280	0.000	0.026	-1	1	0.736	0.736	1.701	0.013	15.144	0.201	See	See
17	KNeighborsClassifier	predict	1000000	1	100	NaN	0.182	0.014	0.000	0.182	-1	1	1.000	1.000	0.091	0.002	1.997	0.159	See	See
18	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.056	0.000	0.287	0.000	1	100	NaN	NaN	0.100	0.000	0.556	0.000	See	See
19	KNeighborsClassifier	predict	1000000	1000	2	NaN	21.278	0.068	0.000	0.021	1	100	0.988	0.976	0.255	0.002	83.445	0.603	See	See
20	KNeighborsClassifier	predict	1000000	1	2	NaN	0.031	0.000	0.000	0.031	1	100	1.000	1.000	0.005	0.000	6.072	0.529	See	See
21	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.056	0.000	0.284	0.000	-1	5	NaN	NaN	0.100	0.000	0.562	0.000	See	See
22	KNeighborsClassifier	predict	1000000	1000	2	NaN	33.511	0.000	0.000	0.034	-1	5	0.986	0.984	0.256	0.001	130.795	0.356	See	See
23	KNeighborsClassifier	predict	1000000	1	2	NaN	0.036	0.001	0.000	0.036	-1	5	1.000	1.000	0.005	0.000	7.203	0.672	See	See
24	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.058	0.000	0.277	0.000	1	1	NaN	NaN	0.100	0.000	0.576	0.000	See	See
25	KNeighborsClassifier	predict	1000000	1000	2	NaN	10.745	0.028	0.000	0.011	1	1	0.977	0.984	0.257	0.001	41.784	0.251	See	See
26	KNeighborsClassifier	predict	1000000	1	2	NaN	0.015	0.001	0.000	0.015	1	1	1.000	1.000	0.005	0.000	2.887	0.243	See	See
27	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.059	0.000	0.272	0.000	-1	100	NaN	NaN	0.100	0.000	0.587	0.000	See	See
28	KNeighborsClassifier	predict	1000000	1000	2	NaN	32.946	0.000	0.000	0.033	-1	100	0.988	0.982	0.302	0.001	108.930	0.348	See	See
29	KNeighborsClassifier	predict	1000000	1	2	NaN	0.048	0.014	0.000	0.048	-1	100	1.000	1.000	0.005	0.001	9.426	2.918	See	See
30	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.065	0.000	0.245	0.000	1	5	NaN	NaN	0.100	0.000	0.651	0.000	See	See
31	KNeighborsClassifier	predict	1000000	1000	2	NaN	21.539	0.383	0.000	0.022	1	5	0.986	0.982	0.302	0.001	71.290	1.289	See	See
32	KNeighborsClassifier	predict	1000000	1	2	NaN	0.031	0.001	0.000	0.031	1	5	1.000	1.000	0.005	0.000	5.973	0.534	See	See
33	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.055	0.000	0.289	0.000	-1	1	NaN	NaN	0.100	0.000	0.555	0.000	See	See
34	KNeighborsClassifier	predict	1000000	1000	2	NaN	23.144	0.382	0.000	0.023	-1	1	0.977	0.976	0.255	0.001	90.774	1.554	See	See
35	KNeighborsClassifier	predict	1000000	1	2	NaN	0.020	0.003	0.000	0.020	-1	1	1.000	1.000	0.005	0.000	3.931	0.686	See	See

KNeighborsClassifier_kd_tree vs daal4py

All estimators share the following hyperparameters:

	value
algorithm	kd_tree

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.769	0.000	0.029	0.000	1	100	NaN	NaN	0.693	0.000	3.994	0.000	See	See
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	4.687	0.048	0.000	0.005	1	100	0.978	0.976	0.189	0.004	24.852	0.546	See	See
2	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.003	0.001	0.000	0.003	1	100	1.000	1.000	0.000	0.000	9.248	5.376	See	See
3	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.773	0.000	0.029	0.000	-1	5	NaN	NaN	0.678	0.000	4.089	0.000	See	See
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.803	0.004	0.000	0.001	-1	5	0.977	0.962	0.101	0.001	7.923	0.083	See	See
5	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.003	0.001	0.000	0.003	-1	5	1.000	1.000	0.000	0.000	9.788	4.609	See	See
6	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.754	0.000	0.029	0.000	1	5	NaN	NaN	0.665	0.000	4.141	0.000	See	See
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	1.382	0.014	0.000	0.001	1	5	0.977	0.976	0.187	0.002	7.372	0.111	See	See
8	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.002	0.000	0.000	0.002	1	5	1.000	1.000	0.000	0.000	4.113	2.297	See	See
9	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.780	0.000	0.029	0.000	-1	100	NaN	NaN	0.670	0.000	4.152	0.000	See	See
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	2.666	0.008	0.000	0.003	-1	100	0.978	0.962	0.103	0.002	25.977	0.401	See	See
11	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.005	0.001	0.000	0.005	-1	100	1.000	1.000	0.000	0.000	16.049	9.087	See	See
12	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.857	0.000	0.028	0.000	-1	1	NaN	NaN	0.662	0.000	4.313	0.000	See	See
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.433	0.004	0.000	0.000	-1	1	0.972	0.976	0.552	0.002	0.784	0.008	See	See
14	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.002	0.000	0.000	0.002	-1	1	1.000	1.000	0.001	0.000	2.777	1.245	See	See
15	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.871	0.000	0.028	0.000	1	1	NaN	NaN	0.665	0.000	4.320	0.000	See	See
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.732	0.004	0.000	0.001	1	1	0.972	0.976	0.552	0.003	1.325	0.010	See	See
17	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.001	0.000	0.000	0.001	1	1	1.000	1.000	0.001	0.000	1.156	0.564	See	See
18	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.741	0.000	0.022	0.000	1	100	NaN	NaN	0.419	0.000	1.769	0.000	See	See
19	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.050	0.002	0.000	0.000	1	100	0.980	0.987	0.001	0.000	45.535	11.399	See	See
20	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	100	1.000	1.000	0.000	0.000	5.307	4.493	See	See
21	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.743	0.000	0.022	0.000	-1	5	NaN	NaN	0.428	0.000	1.735	0.000	See	See
22	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.026	0.001	0.001	0.000	-1	5	0.979	0.978	0.001	0.000	35.735	12.093	See	See
23	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.002	0.000	0.000	0.002	-1	5	1.000	1.000	0.000	0.000	20.547	17.281	See	See
24	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.731	0.000	0.022	0.000	1	5	NaN	NaN	0.424	0.000	1.723	0.000	See	See
25	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.024	0.001	0.001	0.000	1	5	0.979	0.987	0.001	0.000	22.161	6.039	See	See
26	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	5	1.000	1.000	0.000	0.000	5.335	5.024	See	See
27	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.726	0.000	0.022	0.000	-1	100	NaN	NaN	0.453	0.000	1.604	0.000	See	See
28	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.042	0.001	0.000	0.000	-1	100	0.980	0.978	0.001	0.000	58.488	20.091	See	See
29	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.002	0.000	0.000	0.002	-1	100	1.000	1.000	0.000	0.000	18.596	14.627	See	See
30	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.731	0.000	0.022	0.000	-1	1	NaN	NaN	0.430	0.000	1.701	0.000	See	See
31	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.024	0.001	0.001	0.000	-1	1	0.975	0.989	0.006	0.001	3.931	0.556	See	See
32	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.002	0.000	0.000	0.002	-1	1	1.000	1.000	0.000	0.000	17.232	15.216	See	See
33	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.729	0.000	0.022	0.000	1	1	NaN	NaN	0.422	0.000	1.728	0.000	See	See
34	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.023	0.001	0.001	0.000	1	1	0.975	0.989	0.006	0.001	3.764	0.424	See	See
35	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	1	1.000	1.000	0.000	0.000	4.886	3.626	See	See

KMeans_tall vs daal4py

All estimators share the following hyperparameters:

	value
algorithm	full
n_clusters	3
max_iter	30
n_init	1
tol	0.0

	estimator	function	n_samples_train	n_samples	n_features	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KMeans_tall	fit	1000000	1000000	2	30	0.618	0.000	0.776	0.000	k-means++	NaN	30	NaN	0.404	0.0	1.529	0.000	See	See
1	KMeans_tall	predict	1000000	1000	2	30	0.001	0.000	0.380	0.000	k-means++	0.001	30	0.001	0.000	0.0	8.933	5.960	See	See
2	KMeans_tall	predict	1000000	1	2	30	0.001	0.000	0.000	0.001	k-means++	1.000	30	1.000	0.000	0.0	10.869	8.431	See	See
3	KMeans_tall	fit	1000000	1000000	2	30	0.476	0.000	1.009	0.000	random	NaN	30	NaN	0.379	0.0	1.257	0.000	See	See
4	KMeans_tall	predict	1000000	1000	2	30	0.001	0.000	0.379	0.000	random	0.000	30	0.001	0.000	0.0	9.341	6.849	See	See
5	KMeans_tall	predict	1000000	1	2	30	0.001	0.000	0.000	0.001	random	1.000	30	1.000	0.000	0.0	10.341	7.806	See	See
6	KMeans_tall	fit	1000000	1000000	100	30	6.509	0.000	3.687	0.000	k-means++	NaN	30	NaN	3.111	0.0	2.092	0.000	See	See
7	KMeans_tall	predict	1000000	1000	100	30	0.002	0.000	15.409	0.000	k-means++	0.002	30	0.002	0.000	0.0	6.064	3.178	See	See
8	KMeans_tall	predict	1000000	1	100	30	0.002	0.002	0.014	0.002	k-means++	1.000	30	1.000	0.000	0.0	15.034	19.038	See	See
9	KMeans_tall	fit	1000000	1000000	100	30	5.992	0.000	4.005	0.000	random	NaN	30	NaN	2.941	0.0	2.037	0.000	See	See
10	KMeans_tall	predict	1000000	1000	100	30	0.002	0.000	15.852	0.000	random	0.002	30	0.002	0.000	0.0	5.493	2.769	See	See
11	KMeans_tall	predict	1000000	1	100	30	0.001	0.000	0.020	0.001	random	1.000	30	1.000	0.000	0.0	9.792	7.081	See	See

KMeans_short vs daal4py

All estimators share the following hyperparameters:

	value
algorithm	full
n_clusters	300
max_iter	20
n_init	1
tol	0.0

	estimator	function	n_samples_train	n_samples	n_features	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KMeans_short	fit	10000	10000	2	20	0.072	0.0	0.044	0.000	random	NaN	20	NaN	0.030	0.0	2.425	0.000	See	See
1	KMeans_short	predict	10000	1000	2	20	0.002	0.0	0.195	0.000	random	0.003	20	0.001	0.001	0.0	2.626	0.499	See	See
2	KMeans_short	predict	10000	1	2	20	0.001	0.0	0.000	0.001	random	1.000	20	1.000	0.000	0.0	9.162	5.857	See	See
3	KMeans_short	fit	10000	10000	2	20	0.218	0.0	0.015	0.000	k-means++	NaN	20	NaN	0.084	0.0	2.614	0.000	See	See
4	KMeans_short	predict	10000	1000	2	20	0.002	0.0	0.196	0.000	k-means++	0.004	20	0.002	0.001	0.0	2.630	0.613	See	See
5	KMeans_short	predict	10000	1	2	20	0.001	0.0	0.000	0.001	k-means++	1.000	20	1.000	0.000	0.0	10.080	7.472	See	See
6	KMeans_short	fit	10000	10000	100	20	0.189	0.0	0.849	0.000	random	NaN	20	NaN	0.128	0.0	1.478	0.000	See	See
7	KMeans_short	predict	10000	1000	100	20	0.002	0.0	6.681	0.000	random	0.322	20	0.313	0.001	0.0	2.120	0.442	See	See
8	KMeans_short	predict	10000	1	100	20	0.001	0.0	0.012	0.001	random	1.000	20	1.000	0.000	0.0	7.892	4.949	See	See
9	KMeans_short	fit	10000	10000	100	20	0.575	0.0	0.278	0.000	k-means++	NaN	20	NaN	0.308	0.0	1.864	0.000	See	See
10	KMeans_short	predict	10000	1000	100	20	0.002	0.0	6.859	0.000	k-means++	0.295	20	0.319	0.001	0.0	2.127	0.386	See	See
11	KMeans_short	predict	10000	1	100	20	0.001	0.0	0.012	0.001	k-means++	1.000	20	1.000	0.000	0.0	7.582	4.261	See	See

LogisticRegression vs daal4py

All estimators share the following hyperparameters:

	value
penalty	l2
dual	False
tol	0.0001
C	1.0
fit_intercept	True
intercept_scaling	1
class_weight	NaN
random_state	NaN
solver	lbfgs
max_iter	100
multi_class	auto
verbose	0
warm_start	False
n_jobs	NaN
l1_ratio	NaN

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	class_weight	l1_ratio	n_jobs	random_state	accuracy_score	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	LogisticRegression	fit	1000000	1000000	100	[20]	10.974	0.0	[-0.10751501]	0.000	NaN	NaN	NaN	NaN	NaN	2.026	0.0	5.418	0.000	See	See
1	LogisticRegression	predict	1000000	1000	100	[20]	0.000	0.0	[54.57178543]	0.000	NaN	NaN	NaN	NaN	0.531	0.000	0.0	0.893	0.496	See	See
2	LogisticRegression	predict	1000000	1	100	[20]	0.000	0.0	[0.23756355]	0.000	NaN	NaN	NaN	NaN	1.000	0.000	0.0	0.403	0.398	See	See
3	LogisticRegression	fit	1000	1000	10000	[27]	0.789	0.0	[-2.70608541]	0.001	NaN	NaN	NaN	NaN	NaN	0.813	0.0	0.970	0.000	See	See
4	LogisticRegression	predict	1000	100	10000	[27]	0.002	0.0	[137.69279781]	0.000	NaN	NaN	NaN	NaN	0.250	0.003	0.0	0.549	0.123	See	See
5	LogisticRegression	predict	1000	1	10000	[27]	0.000	0.0	[25.51065604]	0.000	NaN	NaN	NaN	NaN	1.000	0.001	0.0	0.124	0.102	See	See

Ridge vs daal4py

All estimators share the following hyperparameters:

	value
alpha	1.0
fit_intercept	True
normalize	deprecated
copy_X	True
max_iter	NaN
tol	0.001
solver	auto
random_state	NaN

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	max_iter	random_state	r2_score	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	Ridge	fit	1000	1000	10000	NaN	0.180	0.0	0.444	0.0	NaN	NaN	NaN	0.183	0.000	0.984	0.000	See	See
1	Ridge	predict	1000	1000	10000	NaN	0.012	0.0	6.661	0.0	NaN	NaN	0.126	0.020	0.001	0.609	0.019	See	See
2	Ridge	predict	1000	1	10000	NaN	0.000	0.0	1.195	0.0	NaN	NaN	NaN	0.000	0.000	0.664	0.695	See	See
3	Ridge	fit	1000000	1000000	100	NaN	1.469	0.0	0.545	0.0	NaN	NaN	NaN	0.247	0.000	5.945	0.000	See	See
4	Ridge	predict	1000000	1000	100	NaN	0.000	0.0	5.322	0.0	NaN	NaN	1.000	0.000	0.000	0.676	0.561	See	See
5	Ridge	predict	1000000	1	100	NaN	0.000	0.0	0.013	0.0	NaN	NaN	NaN	0.000	0.000	0.686	0.709	See	See

Environment information

{
  "system_info": {
    "python": "3.8.10 | packaged by conda-forge | (default, May 11 2021, 07:01:05)  [GCC 9.3.0]",
    "executable": "/usr/share/miniconda/envs/sklbench/bin/python",
    "machine": "Linux-5.4.0-1047-azure-x86_64-with-glibc2.10"
  },
  "dependencies_info": {
    "pip": "21.1.2",
    "setuptools": "49.6.0.post20210108",
    "sklearn": "1.0.dev0",
    "numpy": "1.20.3",
    "scipy": "1.6.3",
    "Cython": null,
    "pandas": "1.2.4",
    "matplotlib": "3.4.2",
    "joblib": "1.0.1",
    "threadpoolctl": "2.1.0"
  },
  "threadpool_info": [
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libopenblasp-r0.3.15.so",
      "prefix": "libopenblas",
      "user_api": "blas",
      "internal_api": "openblas",
      "version": "0.3.15",
      "num_threads": 2,
      "threading_layer": "pthreads"
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/python3.8/site-packages/scikit_learn.libs/libgomp-f7e03b3e.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libgomp.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    }
  ],
  "cpu_count": 2
}